Forecasting is useful in several contexts. In an example context, data planning can improve the balance between item demand and item supply. This task relies on computing accurate forecasts of future item consumption and production at any point in time, and at any level of granularity. The increasing number of new types of items and the increasing accessibility to different markets provide additional challenges to balancing item demand and item supply. Historic time streams of associated item demands can provide useful starting points for optimizing current forecast models. A common technical issue associated to forecasting is that historic time streams are requested and received through multiple calls between systems. For example, the number of calls can be proportional to the number of historic time streams, such that an increase in the number of historic time streams prolongs the processing time associated with a forecast, and otherwise inefficiently consumes computing resources.